However, due to the hardware complexity of low-cost IMUs, it becomes almost infeasible for researchers and engineers to formulate the exact mathematical equations to describe the sensor noise and intrinsic models. Therefore, using data driven methods instead of the model based ones in this domain becomes worthy exploring. Motivated by the recent success of DNN in a variety of tasks, researchers started to look into methods that represent IMU models with DNNs to improve the inertial navigation performance [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
The majority of existing DNN methods are designed to perform the end-to-end IMU integration [1]}, [2]}, [3]}, by taking sensor data as inputs and integrating poses using neural networks.
However, such design regresses both observable or un-observable system terms [10]}, so its robustness can only be guaranteed when patterned motions are involved. Alternatively, there are DNN based methods that target on refining sensor models [4]}, [5]}.
However, both [4]}, [5]} can not be applied to general-case pose integration since they are either optimized for stationary periods or only able to handle rotations.
